2024

Grokking LLMs

I've spent the last year working with LLMs and writing a good amount of technical content on how to use them effectively, mostly with the help of structured parsing using a framework like Instructor. Most of what I know now is self-taught and this is the guide that I wish I had when starting out.

It should take about 10-15 minutes at most to read and I've added some resources along the way that are relevant to you. If you're looking for a higher level, i suggest skimming over the first two sections and then focusing more on the application/data side of things!

I hope that after reading this essay, you walk away with an enthusiasm that these models are going to change so much things that we know today. We have models with reasoning abilities and knowledge capacities that dwarf many humans today in tasks such as Mathetical Reasoning, QnA and more.

Introduction

It's really fun to create your own tools. With some extra time on my hands this weekend, I decided to work on building a small tool that would solve a problem i'd been facing for some time - converting wikilinks to relative links.

For those who are unaware, when you work in tools like Obsidian, the default tends to be wikilinks that look like this [[wiki-link]]. This is great if you're only using obsidian but limits the portability of your markdown script itself. For platforms such as Github, the lack of absolute links means that you can't easily click and navigate between markdown files on their web platform.

Writing scripts that scale

Writing good scripts for machine learning is an art. I struggled with writing them for a long time because of how different it was to my experience working with full-stack frameworks such as React or FastAPI.

There were four main issues that I struggled with

1. My job has a high probability of failing without any reason
2. My data might not fit into memory for no reason
3. Running a single job takes days or more
4. Optimizing hyper-parameters is genuinely difficult

Everything I've learnt about writing good Python code

In the past 6 months, I've 10xed the amount of python code I've written. In this article, I'll show you a few easy actionable tips to write better and more maintainable code. I've been lucky enough to have Jason (@jxnlco on twitter) review a good chunk of my code and I've found that these few things have made a massive difference in my code quality.

1. using the @classmethod decorator
2. learn the stdlib
3. write simpler functions
4. being a bit lazier - earn the abstraction
5. decouple your implementation

Learning with Adult Responsibilities

Introduction

Over the past 6 months, I've been trying to learn more about AI and LLMs. ChatGPT had me hooked when I tried it for the first time. Over the course of this period, I've been chatting to more people, shitposting on twitter and working to learn as much as I can in my spare time.

That amounts to roughly 10-20 hours a week since I don't have much of a social life which has been about 4-500 hours in total since the time I started exploring this space so take my experience with a grain of salt. I'm relatively new and you're probably 2-3 months behind me at most, much less if you do it full time.

I've had some people reach out to me for advice on what to do and I figured I'd write a longer blog post so that I could refer to it myself and consolidate some of my ramblings.